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dc.contributor.authorVargas, S
dc.contributor.authorVera, M I
dc.contributor.authorVera, M
dc.contributor.authorSalazar-Torres, J
dc.contributor.authorHuérfano, Y
dc.contributor.authorValbuena, O
dc.contributor.authorGelvez-Almeida, E
dc.date.accessioned2020-03-26T22:28:12Z
dc.date.available2020-03-26T22:28:12Z
dc.date.issued2019
dc.identifier.issn17426596
dc.identifier.urihttps://hdl.handle.net/20.500.12442/5072
dc.description.abstractAbstract. The mammary pathology can manifest itself in multiple ways and originates spaceoccupying lesions. The breast cancer is a space-occupying lesion, which is highly prevalent, especially in women, and worldwide it is one of the leading causes of morbidity and mortality in this population. The main image modality for breast cancer detection is the magnetic resonance but this kind of image modality introduces several imperfections that affect the image quality. Some of these imperfections or problems are: inhomogeneity in the anatomical structures, riccian noise and artifacts. These problems make the analysis of the image information a real challenge. To address these problems, in this paper, we propose a computational technique able to extract a space-occupying lesion linked to breast cancer, present in magnetic resonance images. For this, the original image is processed with statisticalarithmetic filters and segmentation algorithms based on thresholding and multi-seed region growing techniques. The results, based on Dice score, show that the proposed technique is suitable for segmenting the breast cancer due high correlation between semi-automatic and manual segmentations. This technique can be useful in the detection, characterization and monitoring of this type of cancer and it can let to medical doctors to realize their work more efficiently.eng
dc.format.mimetypepdfeng
dc.language.isoengeng
dc.publisherIOP Publishingeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceJournal of Physics: Conference Serieseng
dc.sourceVol. 1414 (2019)eng
dc.source.urihttps://iopscience.iop.org/article/10.1088/1742-6596/1414/1/012016eng
dc.subjectMammary pathologyeng
dc.subjectBreast cancereng
dc.subjectMagnetic resonance imageseng
dc.titleSpace-occupying lesions identification in mammary glands using a mixed computational strategyeng
dc.typearticleeng
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
oaire.versioninfo:eu-repo/semantics/publishedVersioneng
dc.type.driverarticleeng


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